Existing lane detection methods can be devided into two types. The majority work consider lane detection as an 2D-task driven by image catched by forward-facing camera, whick are based on segmentation. [1]}, [2]}, [3]}, [4]}. In post-processing, the output of the 2D lane detection model is usually projected to the ground plane by IPM (Inverse Perspective Transformation) using the camera intrinsic and extrinsic parameters, and then curve fitting is performed to obtain the lane lines based on the vehicle-ego coordinate system. Based on the ideal case of ground plane hypothesis, the output of 2D methods can get the lane lines similar to real 3D lane lines in the vehicle-ego coordinate system after IPM transformation. However, this approach can cause other problems in the actual driving process [5]}, [3]}, resulting in inaccurate 3D lane lines in the vehicle-ego coordinate system and inaccurate vehicle control. For example, on real roads, uphill and downhill scenarios are frequent. In such scenarios, the 2D lane methods + IPM approach generates incorrect 3D lane predictions, causing the vehicle in autonomous driving to have incorrect driving behavior.
